A quantum simulation of heterogeneous matter, designed by Algorithmiq and executed on IBM quantum computers, continues to compete with the world's leading classical simulation methods eight months after its debut on the Quantum Advantage Tracker.
The work addresses one of quantum computing's central challenges: how to trust a result when no classical computer can verify it.
Algorithmiq is also releasing a new benchmark for quantum advantage claims, making its best classical method for simulating molecular ground states available to the research community.
Read moreWe are releasing monoprop, an open-source engine for classically simulating and variationally optimising quantum circuits. monoprop stands for monomial propagation, the technique the library is built on. It is also a shorthand for monopropellant rockets, given how fast it runs. It is a C++ library with a Python interface that implements both Majorana propagation, a method developed by Algorithmiq's researchers (arXiv:2503.18939), and Pauli propagation (arXiv:2505.21606). To the best of our knowledge, monoprop is the only package that does both. monoprop is the engine behind several of our recent results, which we describe below.
Read moreBack in 2016, DeepMind’s AlphaGo beat the world’s best human player at Go, a game long considered too complex for machines to master. It wasn’t the moment AI suddenly became useful to everyone – we had to wait until 2023 for that – but it showed that a computer could do something that it wasn’t supposed to be able to do.
Read moreItaly’s largest VC investment in a quantum startup backs software as the strategic layer for Europe
Read moreFirst ever company to prove that end-to-end quantum-classical algorithms can
simulate complex therapeutics, marking a clear path to commercially useful quantum
computing.
Read moreEarlier this week, our CEO addressed the United Nations Security Council during a session focused on the future of quantum technologies and their implications for global security and international cooperation. The remarks were delivered as part of a broader discussion among governments, researchers, and industry leaders about how emerging technologies will shape the decades ahead.
Read moreThe collaboration between Algorithmiq and Microsoft will integrate Algorithmiq’s advanced simulation and measurement methods with Microsoft’s Quantum platform and Quantum Development Kit (QDK), enabling the next generation of cloud-deliverable quantum solutions for chemical sciences from drug discovery to materials design.
Read moreAlgorithmiq, in collaboration with IBM, Flatiron Institute, EPFL, and the University of Ljubljana, runs an experiment on an IBM Heron processor modeling operator dynamics in heterogeneous quantum materials, revealing information-flow patterns beyond the reach of classical computation.
Read moreMilestone experiment will appear in the first public tracker of quantum advantage, evolving quantum progress from unsubstantiated claims to an open benchmark system overseen by the academic community.
Read moreQuantum AI is an emerging field at the intersection of quantum computing and artificial intelligence (AI)—two of the most transformative technologies of our time. Bringing the two together is not just theoretical curiosity; it opens new possibilities for solving complex problems at the edge of what classical computers can handle.
Read moreIn the last few years, quantum computing has emerged as a powerful tool with the potential to significantly change numerous industries, with life sciences and healthcare at the forefront [1]. But what are the applications of quantum computing in life sciences? In this post, we will explore the most commonly asked questions about quantum computing for life sciences and provide some answers on the most recent developments in the field.
Read moreWhat is quantum chemistry? Quantum chemistry is a field in which quantum mechanics is used to understand chemical systems. A central problem is solving the Electronic Structure Problem, defined as finding stationary states of the non-relativistic time-independent electronic Schrödinger equation. In the so-called Born-Oppenheimer approximation electron-electron as well as electron-nuclei…
Read moreAlgorithmiq announces a venture to develop NVIDIA-accelerated supercomputing-enabled error mitigation techniques for near-term quantum devices, aiming to accelerate the achievement of quantum advantage.
Read morePress release
16 Sept 2024
Algorithmiq’s breakthrough error mitigation algorithm, TEM, is now commercially available on IBM’s Qiskit Functions Catalog.
Read moreAlgorithmiq’s Tensor-network Error Mitigation (TEM) is a hybrid quantum-classical method for reducing noise on near-term quantum devices. By post-processing informationally complete measurements with tensor networks, TEM improves accuracy without additional quantum circuits. Its favorable scaling and reduced classical cost make it a practical path toward more reliable quantum computations.
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